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What is a Customer Data Platform? CDPs ExplainedWritten by
Matthew Sibun
Matthew SibunHead of Growth Marketing
Brooks Patterson
Brooks PattersonProduct Marketing Manager
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Today’s consumer expects a personalized, end-to-end customer experience. The reward for companies that successfully deliver these experiences is great. Brands that master personalization across their customer experience create a hard-to-copy competitive advantage against companies that struggle to effectively leverage their customer data. According to McKinsey fast-growing companies drive 40% more revenue from personalization than slow growers.

Mastering personalization is not an easy endeavor, however. It requires sophisticated applications of data across different business units and systems. Personalization is, at its core, a data problem that can’t be solved by siloed tools and fragmented data systems. It requires a single customer view, or customer 360.

A customer 360 is a comprehensive, unified view of every relevant data point from every customer that can serve as a single source of truth for every team and every tool. It keeps every team on the same page with rich, up-to-date information about every customer, enabling them to create the personalized experiences customers expect. It’s the key to building strong customer relationships at scale.

The customer data platform (CDP) was created to help companies build this single customer view. Since its formal inception in 2010, the CDP has evolved significantly and today there are many different CDP approaches. Many innovative companies opt for a modern warehouse native. In this post, we’ll answer all of your questions about CDPs to help you get on the path to mastering personalization.

In this article, we'll break down questions like what is a Customer Data Platform (CDP), what does a CDP do, and how to choose a CDP.

What is a Customer Data Platform (CDP)?

In short, a customer data platform (CDP) is a software system that collects customer data, builds a unified view of your customers (by building profiles), and then uses those profiles to add value across the company.

The Customer Data Platform is still a relatively nascent technology, and it’s rapidly evolving. For this reason, it’s helpful to look at the fundamental goal of the CDP in order to fully define it. CDPs exist to help companies drive more business value with their customer data. There are different approaches to achieving this goal, but CDPs typically perform three key functions: data collection, data unification, and data activation. So, what is a CDP?A Customer Data Platform (CDP) is a specialized software platform designed to centralize customer data from various sources, unify that data to build complete customer profiles, and facilitate the activation of those profiles to enrich the quality of customer interactions across multiple touchpoints. The complete customer profile – often called a single customer view or customer 360 – is the central component of the CDP.

Unlike traditional data management tools, a CDP is designed to eliminate data silos by aggregating and organizing data from multiple first and third-party data sources to create these complete customer profiles. Once the profiles are created, the CDP makes them available – either within the CDP itself or in downstream systems – for use in delivering personalized messaging, contextualized interactions, and custom experiences. CDPs enable companies to better understand individual customer behavior, preferences, and interactions to unlock otherwise out-of-reach use cases that drive better business outcomes. This unified view of the customer adds value by:

  • Gaining deeper insights into customers via analytics
  • Enriching the quality of customer interactions
  • Pushing customer intelligence to key teams such as sales, marketing or customer support.
  • Controlling governance and privacy for customer data

While CDPs were initially created for marketing use cases, every business unit can leverage customer data to drive better outcomes. Modern CDPs embrace a “build once, use everywhere” mentality and enable every business unit to take advantage of the CDP’s customer profiles to make sure customer interactions are personalized, consistent across touchpoints, and valuable for all parties involved.

Why Were CDPs Created?

The history of the CDP is best understood when framed by the challenges businesses face in managing customer data. Customer data comes from many sources and is typically scattered across various systems and SaaS platforms. These platforms lock data in their own ecosystems, each creating its own data silo. This leads to a fragmented landscape where different teams and different tools operate on different sources of truth and anything close to a customer 360 is out of reach.

The leverage a company can create with its customer data is severely limited in this scenario. The teams creating and contributing to the customer experience must operate with a limited picture of the customer journey. Inconsistent, impersonal experiences are unavoidable. Without the coveted customer 360, companies struggle with inefficiencies and missed opportunities.

The value of a customer 360 serving as a single source of truth for the entire company, however, has always been apparent. According to Gartner, companies that manage to successfully implement a customer 360 benefit from increased operational efficiency, higher customer satisfaction, and higher profitability. They gain a major competitive advantage over those that fail to implement a customer 360.

With so much opportunity on the table, there has been much innovation aimed at delivering the promise of customer 360. Tooling first emerged somewhat unintentionally, originating as CRMs, infrastructure tools, databases, or tag managers. Eventually, the CDP was formalized as a SaaS tool created primarily for marketing teams. These legacy CDPs promised to automatically bring in first-party data from various sources, structure it in a consistent format, and create comprehensive customer profiles that could be used for analytical and marketing purposes.

Many of these legacy CDPs also provided orchestration tooling on top to enable the tactical activation of data for omnichannel customer engagement. For customer-centric marketing teams, this would help ensure a consistent customer experience for initiatives across web and mobile properties, email, social media, and paid digital advertisements. The issue with these SaaS CDPs, though, is that while they did unify data from other systems, they still created another data silo. There was no easy way for teams outside of marketing to access the valuable metrics stored inside the CDP’s black box.

Today, SaaS tooling is more dynamic. Marketing CDPs do deliver powerful engagement and automation features, but they’re still largely closed, black box, systems. But there’s been a paradigm shift. Innovations around the cloud data warehouse have enabled companies to effectively build a CDP on top of their own data warehouse. David Raab, founder of The CDP Institute said as much: “Modern warehouses, such as Snowflake, use more flexible data stores and can do more things, including much of what would typically be done in a CDP.”

Many innovative companies are now embracing a warehouse native approach to the CDP where customer data is centralized and unified in the data warehouse, then delivered downstream to the tools (including marketing CDPs) business teams use to drive the customer experience.

Understanding what a CDP does

A CDP's goal is to help companies drive more value with their customer data through the activation of complete customer profiles. To achieve this, the CDP must solve each stage of the data activation lifecycle:

The Data Activation Lifecycle

  • Data collection: Customer Data Platforms have integrations with a variety of first-party data sources that capture customer information in relation to an organization. This includes marketing, sales, support, and the product itself.
  • Data unification: Customer Data Platforms build unified customer profiles (oftentimes called “Customer 360” or “Identity Resolution”) that are a combination of static traits as well as aggregate behavior information.
  • Data activation: Once the profiles are built, they are utilized by internal apps, sales/marketing/support vendors, and other systems to take advantage of the data to personalize communications or contextualize interactions.

Data collection

There are several ways of bringing in data, many of which are understood in the context of the modern data stack. These are the most common examples.

  • Application Event Tracking. Organizations can leverage SDKs across a variety of languages that enable them to track and understand user behavior and flows throughout a software application. This can be done either on the client (Mobile, Web, Smart Device) or on the server (Python, Java, Go, etc.) with trade-offs for each.
  • Vendor Webhooks. Some software vendors expose webhooks on activity that happens on their platform. These data streams are private to the organization using the vendor and provide information on how the vendors are used.
  • Production Databases. Production databases are a good source of truth for application transactions or production data models. These data streams are often brought in via ELT/ETL (extract, transform, load) processes that leverage modern techniques like change data capture (CDC).
  • Vendor Data Extracts. Vendors that directly engage with customers on behalf of an organization often hold the data within their systems. This data can be extracted in batch through vendor provided APIs.

Data Unification

Collecting and centralizing your customer data enables you to build unified, comprehensive customer profiles based on every touchpoint with your organization. Given the data that is collected through upstream integrations, a customer data platform builds a single view of the user that can be understood, filtered, aggregated, or propagated, depending on the use case.

Advanced features in building customer profiles include:

  • Personalization. Machine learning models or simple heuristics can help predict what customers are interested in based on existing interaction. This includes personalized recommendations, personalized messaging, or cohort-specific customer journeys. Here's how one of our customers leverages RudderStack and Redis for real-time personalization.
  • Customer Health. Customer health is based on a combination of engagement (or lack thereof), quality of interaction, direct feedback, and more. This information can help guide messaging that is encouraging, conciliatory, determined or exploratory depending on how the customer feels about the company/product.
  • Identity resolution. Customer interactions across many tools often share an identifier, but pseudo-anonymous interactions (e.g. pre-login) benefit from being stitched back together to customers once you determine who they were. Learn more about identity resolution.

Data Activation

Activating data on a customer data platform involves making it available to downstream communication platforms, whether it be sales, marketing, support, or the product itself. These are often the same tools used to capture these interactions from the “data collection” section above.

This can involve either pushing the downstream systems or exposing an interface (e.g. API endpoint) that enables vendors to access and aggregate customer data. If the data lives in a cloud data warehouse, there may be technological overlap with products in the Reverse ETL category which help push data from warehouses to downstream integrations.

In addition to using data for individual customers, CDPs enable organizations to build audiences lists or properties based on features of audiences. For example, if you want to target all customers from Europe who joined in the past 5 days, a CDP would enable you to leverage the central customer profile to build audience lists that can be used in communication platforms.

CDP vs DMP vs CRM: What's the Difference?

There is often confusion around the distinct roles of customer data platforms (CDPs), data management platforms (DMPs), and customer relationship management (CRM) systems. It’s important to clarify their unique functions.


CDPs focus on collecting and unifying first-party customer data to form a comprehensive customer profile, which merges Personally identifiable information (PII), online activities, demographic data, and is often enriched with other third-party data. CDPs are used to enable various teams across a business to understand and act on customer data.


DMPs, on the other hand, mainly collect anonymized web cookie data for use in targeted advertising, typically keeping this data for a short period. DMPs are generally only used by advertising teams.


CRMs maintain customer relationships but typically don't include customer interaction data with a business’s products and services (behavioral data) or transactional details (payments and purchases data).

Data scaleWide and deep spectrum of customer dataAnonymized, based on cookiesFine-grained business data
Typical purposeUnify customer data, gain insights and take actionAchieve better targeting for advertising campaignsTrack status of customer relationships over time
Most common usersWidespread usage by anyone who consumes customer data internallyPaid advertising teamsSales and customer support teams
Data sourcesPrimarily first part dataPrimarily closed third party data setsFirst party data, often enriched with public data sets

The data that makes up a CDP

Your CDP can’t create customer profiles without customer data. Building a customer 360, begins with collecting data from every customer touchpoint and augmenting it with available enrichment data. The exact makeup of customer profiles will vary across businesses and industries, and no one knows your specific needs like you do, but it’s helpful to consider the basic building blocks.

First-party data is the most critical data component when it comes to your customer profiles. You should prioritize this as the foundation of your customer 360, but a comprehensive picture of the customer typically includes data from available second and third-party sources. Below, we’ll provide an overview of each data source:

  • First-party user behavior data – Called clickstream or event data, this data gets collected in real-time and represents your customer’s digital customer journey. It includes all of the actions taken by your users across platforms and devices.
  • First-party batch data – Often thought of as traditional ETL data, batch data includes all of the customer information stored in cloud SaaS tools and databases such as your CRM or support desk tool.
  • Second and third-party data – This data can be grouped together and called enrichment data. It augments your first-party data to give you more comprehensive customer profiles. Second-party data might come from ad platforms and shipping/delivery systems while third-party data, purchased from data vendors, can complete your customer picture with browsing, intent, and demo/firmographic data.

Types of CDP

According to the CDP Institute's classification, Customer Data Platforms can be categorized into several distinct types, each with its unique focus and capabilities:

  • Data CDPs: These platforms primarily focus on accumulating data from various sources, aligning this data with individual customer profiles, and then making it accessible to other business applications in the form of audience segments. They play a crucial role in ensuring that customer data is effectively captured and organized for further use.
  • Analytics CDPs: Going beyond data collection and assembly, Analytics CDPs enhance their functionality with advanced features like machine learning, customer journey mapping, predictive modeling, and revenue attribution. These platforms are integral in deriving deeper insights from customer data, enabling businesses to make more informed decisions.
  • Campaign CDPs: Specializing in segmentation, Campaign CDPs focus on analytics and specific customer treatments. They are instrumental in orchestrating customer interactions across various marketing channels. This includes personalized messaging, outbound marketing campaigns, real-time interactions, and tailored recommendations, ensuring a cohesive and customized marketing approach.
  • Delivery CDPs: While encompassing all the standard capabilities of a traditional CDP, Delivery CDPs specialize in the distribution of messaging through various channels such as email, websites, mobile apps, advertising platforms, and CRMs. This specialization allows for a targeted and efficient communication strategy.

CDPs vs. Data Warehouses vs. Data Lakes

We’ve covered both data warehouses and data lakes elsewhere, but it’s worth the effort to summarize the primary differences.

CDPs primarily deal with first-party customer data; that is, data that was either supplied directly by the customer, or inferred from their actions and behavior within your product or service offering. The collected data is specifically organized to allow business users to generate actionable insights into customer behavior.

The core value a CDP brings to a software product is its integration across the data stack. By relying on a CDP, it’s possible to identify users, create user profiles, and keep track of users’ behavior and preferences in various parts of the application, from the front end to each back-end microservice.

Data warehouses can manage structured data of almost any scale. The data stored in a warehouse is usually extracted from a variety of sources, but before storage, the data is transformed into a standardized format.

The most valuable aspect of a data warehouse is the structure it provides: it allows the data from the warehouse to be used directly as an input into artificial intelligence prediction models, for business intelligence analysis, and for other business purposes.

Data lakes can manage multiple kinds of unstructured data. The data is made available for quick ad hoc queries, but usually requires further processing before it can be used in other systems and business processes. What makes data lakes uniquely valuable is the high volume of storage they provide, as well as the ability to manage and query unstructured data at low cost.

How to choose a CDP

The best customer data platform for you depends on a number of factors you may value for your business. Data storage, privacy, control, cost, completeness, extensibility, and more. Here are a few considerations that should inform your CDP evaluation:

  • How complete is the data coming in? With the new wave of privacy-conscious browser restrictions designed to improve user privacy and to weaken the marketplace of data brokers, first-party event collection systems are often affected even though they are not the intended target. Effective CDPs have a number of approaches to tackling external challenges and verifying data quality.
  • How good is the data coming in? Do you need tight controls over the data quality of the data coming in from your application, your vendors, and your databases? Some CDPs support a concept of Tracking Plans to verify that data coming in has a consistent format that can be used to accurately update a user profile.
  • How much control do you have over your data? When working with a customer data platform, do you need to interact with the data directly, or are you ok working with the data through interfaces provided by the CDP? If a CDP shares its data with you or lives on a cloud data warehouse, then you have more visibility into your data and can re-use your customer data profiles for custom processing and analytics. Read more on data control.
  • What channels do you use to communicate with customers? B2B organizations have high-value investments in a smaller number of customers through sales processes, while B2C (such as ecommerce or marketplaces) organizations traditionally invest more in digital marketing and advertisements. Different CDPs have product optimizations or cost models that may be more advantageous for one or the other.
  • How comprehensive is your data privacy program? Data privacy laws such as GDPR and CCPA put stricter controls on the collection, access, storage, and facilitation of customer data. A CDP should be able to provide compliance for both data within its system as well as forward deletion requests to downstream systems. In addition to this, some CDPs may go one step further with compliance for medical data (HIPAA). Read more on the role your CDP can play in data privacy.

Ultimately, the customer data platform is an evolving concept that brings together multiple elements of the modern data stack into a very clear set of goals that enable organizations to make the most out of first-party customer data.

CDP Business Rationale: Build vs. Buy

The underlying goal for most businesses implementing a CDP is to improve their customers’ experience. While such an improvement sounds straightforward, it’s everything but straightforward for a large business with thousands of employees and numerous stakeholders involved in business decisions.

Soumyadeb Mitra, RudderStack’s CEO, goes into the details of how to address this challenge in the post Build or Buy? Lessons From Ten Years Building Customer Data Pipelines. In short, to give the right teams the necessary information about what needs to be changed to improve customer experience, the following steps are needed:

  1. Collect information about each interaction a customer has with your business.
  2. Unify that data and store it in a centralized location.
  3. Enrich the data and integrate it into other tools used by product, development, marketing, and sales teams.

Once the decision is made on implementing a CDP, there is another important decision to make: should you build your own CDP, or should you implement a third-party product instead?

For those choosing to build, the common challenges include:

  • Scaling the infrastructure to support millions of events per day and more.
  • Integrating the CDP with all the necessary data sources, such as databases, other SaaS products, and advertising platforms like Google Ads.
  • Transforming the data into a standardized format and ensuring the data is being updated correctly with new events.
  • Dealing with privacy regulations that affect customer data.
  • Being able to support internal demand for new functionality or integrations, while maintaining existing ones (e.g. changes in destination API versions, deprecated endpoints, etc.)

Those buying a third-party solution will likely run into issues like:

  • Data lock-in: inability to fully leverage the data you collect because of how and where it’s stored by the vendor’s product.
  • Lack of customization options and support for complex use cases.
  • High cost, especially for vendors that both process and store your data.
  • High cost of switching: Since CDP data collection and destinations are often core to supporting business processes, replacing such a core component of your business requires significant planning and resources.
  • Potential regulatory requirements: If you cannot send, store, or process your data outside your own infrastructure, certain types of CDPs may not even be an option in the first place.

The challenges of building your own CDP are highly complex, and using third-party software can be a way to avoid the headache of self-building. It’s worth considering whether you have the resources to build your own CDP, as well as evaluating the cost of any third-party software and the level of control you will have over it, before making a decision.

How other companies use CDPs

With all of the hype around customer 360s and CDPs, it’s fair if it all seems a little overblown. With all of the challenges related to leveraging customer data, it’s also fair if success seems out of reach. But the truth is that success is attainable, today more than ever, and when you succeed, you can drive a sustained competitive advantage for your company. You don’t have to take our word for it, here’s how our customers are driving better outcomes with a Warehouse Native CDP:

  • Wyze Delivers AI-driven Campaigns With RudderStack Profiles and Snowflake – Wyze is now delivering 3x more ML-driven marketing campaigns with RudderStack thanks to a more efficient data function. With data collection and unification solved, data engineering increased productivity 10x. Similarly, the AI team’s productivity has tripled because they’re getting better data to start with, and it’s easier for them to quickly define new features.

RudderStack’s warehouse native approach eliminated the manual processes bogging down our data engineers. With clean data at their disposal and automated workflows to route it downstream, they started providing our AI/ML, marketing, and product teams with actionable information to drive new models and power new processes.

Wei Zhou, Director of Data Engineering at Wyze
  • Joybird overcomes data integration challenges – Joybird streamlined data collection and activation with RudderStack, enabling marketing to spin up new, personalized campaigns with valuable data from their Snowflake data warehouse in one hour, a process that used to take two weeks.

RudderStack’s warehouse-first approach gives us the best of both worlds. We have the event data streams that we can activate in real-time. We also send the event data to Snowflake and join it with data from different CRM services to create a richer customer profile that we can then send to downstream destinations such as Iterable via Reverse-ETL.

Brett Trani, Director of Analytics at Joybird
  • HealthMatch Builds a Scalable HIPAA-Compliant Tech Stack – Healthmatch created a new customer engagement workflow and unlocked full customer journey analytics with RudderStack. Best of all, because of our Warehouse Native approach they did it with a fully HIPAA-compliant stack.

HealthMatch is a small company with a big idea. We match over one million patients with 300 plus medical conditions with clinical trial sponsors and researchers in four countries. Despite our vision, we were hampered by our limited technology stack comprising SendGrid and Google Analytics. Over seven days, we built a full-blown customer engagement system using Customer.io and RudderStack.

HealthMatch Product Lead, Joel Pinkham

Next steps in customer data platform development

Properly planned CDPs give your business an edge in establishing, maintaining, and refining the customer experience and driving increased sales and profit through better-developed leads. It is also important to think about CDPs in the larger context of Customer Data Infrastructure (CDI). A CDP is the engine powering your data collection and processing, but there is also data storage, data modeling, data pipelines, and more to think about for a robust tech stack.

Build a world-class customer experience with the Warehouse Native CDP

The Superior CDP Architecture of RudderStack's Warehouse Native Approach

If you want to solve issues around data integration, build truly comprehensive customer profiles, and enable every team in your organization to use them to drive better outcomes, you need a CDP. No matter where you are on your data maturity journey, and even if you don’t have a data warehouse, RudderStack, a leading alternative to Segment, can help you collect comprehensive data about your customers and use it make an impact.

You can also learn more about customer data in our corpus of related articles, including:

Schedule a demo with our team today to learn more about how a CDP can help you.

December 15, 2023